The Differences in Health Care Utilization
between Medical Aid and Health Insurance: A
Longitudinal Study Using Propensity Score
Matching
Jae-Hyun Kim1,2, Kwang-Soo Lee3, Ki-Bong Yoo4, Eun-Cheol Park2,5*
1Department of Public Health, Graduate School, Yonsei University, Seoul, South Korea,2Institute of Health Services Research, Yonsei University, Seoul, South Korea,3Department of Health Administration, College of Health Sciences, Yonsei University, Wonju, Gwangwondo, South Korea,4Department of Healthcare Management, Eulji University, Sungnam, Korea,5Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea
*ecpark@yuhs.ac
Abstract
Study Objectives
Health care utilization has progressively increased, especially among Medical Aid benefi-ciaries in South Korea. The Medical Aid classifies benefibenefi-ciaries into two categories, type 1 and 2, on the basis of being incapable (those under 18 or over 65 years of age, or disabled) or capable of working, respectively. Medical Aid has a high possibility for health care utiliza-tion due to high coverage level. In South Korea, the nautiliza-tional health insurance (NHI) achieved very short time to establish coverage for the entire Korean population. However there there remaine a number of problems to be solved. Therefore, the objective of this study was to investigate the differences in health care utilization between Medical Aid bene-ficiaries and Health Insurance benebene-ficiaries.
Methods & Design
Data were collected from the Korean Welfare Panel Study from 2008 to 2012 using propen-sity score matching. Of the 2,316 research subjects, 579 had Medical Aid and 1,737 had health insurance. We also analyzed three dependent variables: days spent in the hospital, number of outpatient visits, and hospitalizations per year. Analysis of variance and longitu-dinal data analysis were used.
Results
The number of outpatient visits was 1.431 times higher (p<0.0001) in Medical Aid beneficia-ries, the number of hospitalizations per year was 1.604 times higher (p<0.0001) in Medical Aid beneficiaries, and the number of days spent in the hospital per year was 1.282 times higher (p<0.268) for Medical Aid beneficiaries than in individuals with Health Insurance.
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OPEN ACCESS
Citation:Kim JH, Lee KS, Yoo KB, Park EC (2015) The Differences in Health Care Utilization between Medical Aid and Health Insurance: A Longitudinal Study Using Propensity Score Matching. PLoS ONE 10(3): e0119939. doi:10.1371/journal.pone.0119939
Academic Editor:Joan A Caylà, Public Health Agency of Barcelona, SPAIN
Received:June 26, 2014
Accepted:January 26, 2015
Published:March 27, 2015
Copyright:© 2015 Kim et al. This is an open access article distributed under the terms of theCreative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability Statement:The authors do not own the data underlying this study. The data is available athttp://www.koweps.re.kr/data/data/list.do. The authors used wave 1 to wave 7.
Funding:The authors have no support or funding to report.
Medical Aid patients had a 0.874 times lower frequency of having an unmet needs due to economic barrier (95% confidence interval: 0.662-1.156).
Conclusions
Health insurance coverage has an impact on health care utilization. More health care utiliza-tion among Medical Aid beneficiaries appears to have a high possibility of a moral hazard risk under the Health Insurance program. Therefore, the moral hazard for Medical Aid bene-ficiaries should be avoided.
Introduction
The national health insurance (NHI) system was introduced in South Korea in 1977 and it cov-ered the entire population in July 1989 [1].
Even though South Korea achieved universal coverage through the mandatory NHI pro-gram, public expenditure spent on NHI program as a proportion of total health expenditure was only 59%. This is in contrast to an average of 77% among member countries of the Organi-zation for Economic Co-operation and Development (OECD) [2]. Moreover, South Korea faces the highest rate of growth in health spending, which is more than twice that of the OECD average (Fig. 1) [2,3].
One of the reasons for these phenomena is a low contribution rate to the NHI (5.89% of payroll income, 2013), which was induced from a low economic level in South Korea when the NHI started back in 1977, in addition to low contribution rates for developing non-covered ser-vices from medical care providers [4]. High out-of-pocket expenses result from copayment for insured services, full payment for non-covered services, and health care provider
Fig 1. Health expenditure growth rates since 2004, Korea and OECD average.
reimbursement for fee-for-service payments [2]. Thus, out-of-pocket expenses of individual beneficiaries have increased in spite of some efforts of expanding benefit coverage from the government, and the medical practice patterns are distorted because of the disproportionate expansion of non-covered services. As a result, benefits are relatively low and public funding is limited, leaving beneficiaries with relatively high co-payments [2].
Approximately 96% of South Korea’s population was covered by the NHI in 2007. The re-maining 4% were covered by a separate program called Medical Aid, which is a public assis-tance program targeted at poor individuals who are recipients of the National Basic Livelihood Security System in South Korea as a part of the social welfare programs [5]. As of December 2009, Medical Aid beneficiaries represented about 3.8% of the country’s population [5]. The Medical Aid program classifies beneficiaries into two categories, type 1 and 2, on the basis of being incapable (those under 18 or over 65 years of age, or disabled) or capable of working, re-spectively. The cost of Medical Aid payments accounts for 16.9% of the total NHI expenditure, and has increased by an average of 15.9% year-to-year in the period of 2002 to 2006 [6]. Simi-larly, the number of Medical Aid beneficiaries increased by 4.5% in the 2005 to 2008 period. The national expenditure for the Medical Aid beneficiaries increased by 71.7% in the same pe-riod in South Korea, indicating that the rapid increase of Medical Aid expenditure may threat-en other social welfare programs [5]. Therefore, the objective of this study was to investigate the differences in health care utilization among individuals who are covered by Medical Aid and Health Insurance.
Methods
Study Sample
The study data used Korean Welfare Panel Study (KOWEPS) from 2008 to 2012. Conducted by the Korea Institute for Health and Social Affairs and Seoul National University, the
KOWEPS is an annual longitudinal panel survey that began in 2006 that uses proportional sys-tematic stratified cluster sampling to select a representative sample of households in South Korea as a whole. Multiple interviews were conducted in the same households, so all members of the household 15 years or older completed the individual questionnaire when possible.
The panel consisted of 18,856 individuals from a national probability sample of 7,072 households residing in South Korea who have been surveyed annually since 2006. The sample was selected by using systematic two-stage stratified cluster sampling from 2005 census data. The KOWEPS included post-stratification weights based on 2005 census data; it was weighted by a primary sampling unit and for the intentional oversampling of low-income households. Further details of the sampling design, methods, and data sets can be found elsewhere (http://
www.koweps.re.kr/). No ethical approval was needed because data for the current study are
publicly available.
Respondent samples were collected from a total of 16,613 individuals from 6,314 house-holds (wave 3), 16,255 individuals from 6,207 househouse-holds (wave 4), 15,625 individuals from 6,207 households (wave 5), 14,696 individuals from 6,034 households (wave 6), and 14,604 in-dividuals from 5,735 households (wave 7).
Study Variables
To investigate the difference in health care utilization between Medical Aid beneficiaries and Health Insurance beneficiaries, our target population consisted of those who had no health in-surance status change for 5 years among both Health Inin-surance beneficiaries and Medical Aid beneficiaries. We extracted a study sample using a 1:3 propensity score adjusting for the num-ber of chronic diseases and ordinary income by health insurance status in wave 3 (2008), and then merged individuals from wave 4 (2009) to wave 7 (2012). In this study, we analyzed three dependent variables: days spent in the hospital, number of outpatient visits, and hospitaliza-tions per year through longitudinal data analysis using negative binomial function because health care utilization had skewed distribution. We also used repeated-measurement model (GLIMMIX model) and it determine whether probability of health care utilization changed over time, annually.
Age, gender, education level, residential region, marital status, economic activity status, number of chronic diseases, and unmet needs due to economic barrier and duration of medical treatment were included in the analyses as covariates. Education level was categorized into four groups: elementary school or lower, middle school, high school, and college or higher. Residen-tial regions were categorized as urban (Seoul, Daejeon, Daegu, Busan, Incheon, Kwangju, or Ulsan) or rural (not classified as a city). Economic activity status was divided into two catego-ries: yes (White colar, Blue colar) or no (housewife, students). and no (including housewives and students). Individuals were classified as married or single; the latter group included the previously married, the widowed, and the divorced. The number of chronic diseases and per-ceived health status were also included in our models. The number of chronic diseases was operationalized into three different categories: 0 1 and2.
Self-reported data regarding unmet needs due to economic barrier was extracted from the response to the question:“Have you ever experienced not going to the hospital due to having no money during the year?”An unmet needs due to economic barrier was categorized as either
“yes”or“no.”
Statistical Analysis
Analysis of variance and longitudinal data analysis were used to investigate the association be-tween health insurance status and health care utilization (i.e. number of days spent in the hos-pital, outpatient visits, and hospitalizations per year).
We conducted a longitudinal data analysis through Proc genmod procedure with the nega-tive binomial function to investigate the number of outpatient visits and hospitalizations per year and days spent in the hospital per year because health care utilization variables had skewed distribution. In addition, we also ran a generalized linear mixed model with the binary distribu-tion to investigate the associadistribu-tion between unmet needs due to economic barrier and health insurance coverage.
Generalized linear models apply when the data are uncorrelated. In many studies, observa-tions exhibit some form of dependency. For example, measurements of different attributes are taken from the same patient, observations are collected over time, sampling or randomization is carried out hiearchically, and so forth.
The models fit by the GLIMMIX procedure extend the GLM by incorporating correlations among the responses. This can be accomplished by including random effects in the linear pre-dictor and/or by modeling the correlations among the data directly. The GLIMMIX procedure distinguishes the two approaches as“G-side”and“R-side”random effects.
type of insuranceis the dependent variables
β0is the intercept
Xiis the covariates eiis the error term irepresents each subject
This terminology draws on a common specification of the linear mixed model,
Yit ¼ ðb0þmiÞ þ b1type of insuranceitþXitþeit
whereYitis the dependent variable (i.e., Number of outpatient visits, number of hospitaliza-tions, days spent in hospital) during a time periodtfor uniti.
Yis the dependent variables
β0is the intercept
μiis the random effects error term
type of insuranceis the type of insurance
Xis the covariates
eitis the error term irepresents each subject
trepresents time period
The criterion for significance was p0.05, two-tailed. All analyses were conducted using the SAS statistical software package version 9.2 (SAS Institute Inc., Cary, NC, USA).
Propensity score matching
Propensity score matching (PSM) is a statistical matching technique that is being used in ob-servational studies to reduce bias. We performs a 1:3 case-control match on the propensity score and it makes best matches first and next-best matches next, in a hierarchical sequence until no more matches can be made (nearest neighbor matching). SAS LOGISTIC procedure code was used to create the propensity score [7].
Results
Table 1shows the number of observation included in this study, annually andTable 2lists the
general characteristics of the subjects at baseline (2008) before propensity matching.Table 3
shows the general characteristics of the subjects at baseline (2008) after propensity matching adjusting for all variables included. Of the 2,072 research subjects, 1,554 (75.0%) had Health Insurance benefits and 518 (25.0%) received Medical Aid benefits. In the Medical Aid group the mean number of outpatient visits was 28.91 [standard deviation (SD): 40.57] per year, the mean number of hospitalizations was 0.23 (SD: 0.7) per year, and the mean number of days spent in the hospital was 3.66 (SD: 16.04) per year. In the Health Insurance group, the mean
Table 1. Number of observations included in this study.
Number of individuals Number of households Number of observations included
2008 (wave 3) 16,613 6,314 2,072 (Medical Aid: 518; Health insurance: 1,554)
2009 (wave 4) 16,255 6,207 2,072 (Medical Aid: 518; Health insurance: 1,554)
2010 (wave 5) 15,625 6,207 2,072 (Medical Aid: 518; Health insurance: 1,554)
2011 (wave 6) 14,696 6,034 2,072 (Medical Aid: 518; Health insurance: 1,554)
2012 (wave 7) 14,604 5,735 2,072 (Medical Aid: 518; Health insurance: 1,554)
Table 2. General characteristics of the study subjects at the baseline (2008) before propensity matching method.
Total Medical Aid Health insurance P-value
Mean SD Mean SD Mean SD
Number of outpatient visit 14.23 25.22 28.25 39.31 13.53 24.10 <.0001
Number of hospitalization 0.13 0.52 0.23 0.68 0.12 0.51 0.000
Days spent in hospital 1.80 10.74 3.49 15.38 1.72 10.45 0.007
N % N % N % P-value
Gender 0.001
Male 5,495 45.3 220 4.0 5,275 96.0
Female 6,643 54.7 356 5.4 6,287 94.6
Age <.0001
19 2,675 22.0 125 4.7 2,550 95.3
20–39 2,735 22.5 42 1.5 2,693 98.5
40–59 3,125 25.8 154 4.9 2,971 95.1
60 3,603 29.7 255 7.1 3,348 92.9
Residential region <.0001
Urban 5,199 42.8 301 5.8 4,898 94.2
Rural 6,939 57.2 275 4.0 6,664 96.0
Marital status <.0001
Married 6,611 54.5 189 2.9 6,422 97.1
Single 5,527 45.5 387 7.0 5,140 93.0
Education level <.0001
Elementary 4,971 41.0 351 7.1 4,620 92.9
Middle school 1,623 13.4 101 6.2 1,522 93.8
High school 3,067 25.3 103 3.4 2,964 96.6
College 2,477 20.4 21 0.9 2,456 99.2
Ordinary income <.0001
Low 4,431 36.5 546 12.3 3,885 87.7
Middle 4,350 35.8 30 0.7 4,320 99.3
High 3,357 27.7 - - 3,357 100.0
Number of chronic disease <.0001
No 6,625 54.6 145 2.2 6,480 97.8
Yes 5,513 45.4 431 7.8 5,082 92.2
Duration of medical treatment <.0001
Nothing 7,308 60.2 163 2.2 7,145 97.8
3 month or less 423 3.5 18 4.3 405 95.7
3–6 month 152 1.3 10 6.6 142 93.4
6 month or more 4,255 35.1 385 9.1 3,870 91.0
Unmet need experience <.0001
Yes 285 2.4 50 17.5 235 82.5
No 11,853 97.7 526 4.4 11,327 95.6
Economic activity status <.0001
No (including housewife) 7,073 58.3 471 6.7 6,602 93.3
White collar 2,394 19.7 31 1.3 2,363 98.7
Blue collar 2,671 22.0 74 2.8 2,597 97.2
Total 12,138 100.0 576 100.0 11,562 100.0
Table 3. General characteristics of the study subjects at the baseline (2008) after propensity matching method.
Total Medical Aid Health insurance P-value
Mean SD Mean SD Mean SD
Number of outpatient visit 23.64 35.11 28.91 40.57 21.88 32.92 0.000
Number of hospitalization 0.17 0.56 0.23 0.70 0.15 0.50 0.012
Days spent in hospital 3.33 17.75 3.66 16.04 3.22 18.28 0.626
N % N % N % P-value
Gender 1.000
Male 768 37.1 192 25.0 576 75.0
Female 1,304 62.9 326 25.0 978 75.0
Age <.0001
19 478 23.1 98 20.5 380 79.5
20–39 142 6.9 31 21.8 111 78.2
40–59 419 20.2 144 34.4 275 65.6
60 1,033 49.9 245 23.7 788 76.3
Residential region 0.374
Urban 993 47.9 257 25.9 736 74.1
Rural 1,079 52.1 261 24.2 818 75.8
Marital status 0.188
Married 1,377 66.5 332 24.1 1,045 75.9
Single 695 33.5 186 26.8 509 73.2
Education level 0.212
Elementary 1,327 64.0 322 24.3 1,005 75.7
Middle school 316 15.3 87 27.5 229 72.5
High school 324 15.6 89 27.5 235 72.5
College 105 5.1 20 19.1 85 81.0
Ordinary income 0.914
Low 1,950 94.1 488 25.0 1,462 75.0
Middle 122 5.9 30 24.6 92 75.4
High - - -
-Number of chronic disease 0.061
No 628 30.3 140 22.3 488 77.7
Yes 1,444 69.7 378 26.2 1,066 73.8
Duration of medical treatment 0.367
Nothing 692 33.4 158 22.8 534 77.2
3 month or less 57 2.8 15 26.3 42 73.7
3–6 month 21 1.0 4 19.1 17 81.0
6 month or more 1,302 62.8 341 26.2 961 73.8
Unmet need experience 0.584
Yes 118 5.7 32 27.1 86 72.9
No 1,954 94.3 486 24.9 1,468 75.1
Economic activity status 0.239
No (including housewife) 1,623 78.3 414 25.5 1,209 74.5
White collar 155 7.5 30 19.4 125 80.7
Blue collar 294 14.2 74 25.2 220 74.8
Total 2,072 100.0 518 25.0 1,554 75.0
number of outpatient visits was 21.88 (SD: 32.92) per year, the mean number of hospitaliza-tions was 0.15 (SD: 0.50) per year, and the mean number of days spent in the hospital was 3.22 (SD: 18.28) per year.
Table 4shows the association between variables and health care utilization from 2008. The
mean number of outpatient visits for Health Insurance beneficiaries was 23.64 (SD: 35.11) per year. The mean number of outpatient visits for Medical Aid beneficiaries was 28.91 (SD: 40.57) per year. The mean number of hospitalizations for Health Insurance beneficiaries was 0.15 (SD: 0.50) per year. The mean number of hospitalizations for Medical Aid beneficiaries was 0.23 (SD: 0.7) per year. The mean number of days spent in the hospital for Health Insurance beneficiaries was 3.22 (SD: 18.28) per year. The mean number of days spent in the hospital for Medical Aid beneficiaries was 3.66 (SD: 16.04) per year.
The number of outpatient visits per year was 1.431 times higher (p<0.0001) for Medical Aid beneficiaries than for individuals with Health Insurance. The number of hospitalizations per year was 1.604 times higher (p<0.0001) for Medical Aid beneficiaries than for individuals with Health Insurance. The number of days spent in the hospital per year was 1.282 times higher (p<0.268) for Medical Aid beneficiaries than for individuals with Health Insurance
(Table 5).Table 6shows the adjusted effect of subgroup analysis according to economic
activety status.
Additionally, we analyzed whether an unmet needs due to economic barrier for Medical Aid beneficiaries existed (Appendix 1). The odds of an unmet needs due to economic barrier for Medical Aid beneficiaries was 0.875 times lower (95% confidence interval: 0.662–1.156) com-pared with Health Insurance beneficiaries.
Discussion
The results of this study show that Medical Aid beneficiaries tended to have an increased num-ber of outpatient visits, hospitalizations, and days spent in hospitals compared to Health Insur-ance beneficiaries who have relatively lower medical service coverage. Additionally, our results seem to indicate that an unmet needs due to economic barrier for Medical Aid beneficiaries on medical care utilization does not exist. These associations were independent of sociodemo-graphic variables (age, gender, residential region, marital status, education level, and employ-ment status), health status variables (number of chronic diseases), unmet needs due to economic barrier, and year.
Medical Aid in South Korea was started in 1977 as a way to serve the underprivileged popu-lation [5]. However, beneficiaries of Medical Aid increased concurrent to national expenditures for the program [5]. The characteristics of health care utilization by Medical Aid beneficiaries also revealed that the per capita cost of health care services for individual beneficiaries was 3.62 times that of NHI policyholders [5].
Health expenditure per capita (US dollar) in South Korea was last measured at 1438.78 in 2010 and 1702.58 in 2012, according to the World Bank and has gradually increased [8].
Table 4. Association between variables and health care utilization at baseline (2008).
Number of outpatient visit
P-value Number of hospitalization
P-value Days spent in hospital
P-value
N Mean SD Mean SD Mean SD
Medical Security <.0001 0.003 0.626
Health Insurance 1,554 21.88 32.92 0.15 0.50 3.22 18.28
Medical Aid 518 28.91 40.57 0.23 0.70 3.66 16.04
Gender <.0001 0.757 0.004
Male 768 17.76 29.70 0.18 0.57 4.79 26.16
Female 1,304 27.10 37.52 0.17 0.55 2.47 9.78
Age <.0001 <.0001 0.001
19 478 8.98 16.76 0.06 0.36 0.90 7.06
20–39 142 6.72 10.61 0.12 0.37 7.48 39.81
40–59 419 20.12 28.71 0.20 0.68 3.34 17.53
60 1,033 34.17 41.71 0.22 0.59 3.88 16.20
Residential region 0.023 0.446 0.899
Urban 993 21.81 30.97 0.16 0.52 3.28 17.86
Rural 1,079 25.32 38.47 0.18 0.59 3.38 17.65
Marital status 0.061 0.149 0.011
Married 695 25.68 36.26 0.20 0.55 4.73 25.31
Single 1,377 22.61 34.49 0.16 0.56 2.63 12.23
Education level <.0001 0.728 0.626
Elementary 1,327 28.17 38.89 0.18 0.59 2.98 11.53
Middle school 316 17.84 29.74 0.16 0.46 3.57 25.96
High school 324 14.97 23.07 0.14 0.54 4.25 24.83
College 105 10.56 12.87 0.16 0.50 4.28 24.80
Ordinary income 0.004 0.0036 0.7422
Low 1,950 24.20 35.37 0.17 0.56 3.36 17.98
Middle 122 14.66 29.41 0.12 0.42 2.82 13.49
High - - -
-Number of chronic disease <.0001 <.0001 <.0001
No 628 5.25 8.25 0.02 0.14 0.32 4.06
Yes 1,444 31.63 39.10 0.24 0.65 4.64 20.96
Duration of medical treatment <.0001 <.0001 0.0002
Nothing 692 5.89 9.39 0.04 0.21 0.88 10.52
3 month or less 57 13.54 12.18 0.23 0.66 3.86 17.22
3–6 month 21 34.29 33.73 0.24 0.44 4.24 9.86
6 month or more 1,302 33.34 40.30 0.24 0.66 4.60 20.58
Economic activity status 0.121 0.237 0.045
No (including housewife) 1,623 24.35 35.37 0.18 0.60 3.84 19.79
White collar 155 18.65 31.81 0.13 0.36 2.01 7.74
Blue collar 294 22.32 35.18 0.13 0.39 1.25 4.35
Unmet need experience 0.245 0.132 0.507
Yes 118 27.29 42.53 0.25 0.55 2.28 7.11
No 1,954 23.42 34.61 0.17 0.56 3.40 18.19
Total 2,072 23.64 35.11 0.17 0.56 3.33 17.75
In the present study, characteristics related to overuse showed chronic disease, and income. These findings are similar to those of Hadley and Holahan[10], who showed that recipients of Medicaid in the US were poorer and had more diseases and disability than those with low in-comes who had private insurers. Half of those in Hadley and Holahan’s study had a cognitive disorder, and they had four times as many physical health limitations as those with low income who had private insurers.
The increase in Medical Aid expenditure may be explained by the behaviors of both recipi-ents and providers. Specifically, two factors appear to have possibly contributed to this dramat-ic increase. First, free or much cheaper meddramat-ical servdramat-ice than those covered by standard health insurance could have led recipients to visit clinics more freely [11], whereas full reimbursement for providers’services might have led them to order frequent clinic visits and [11]. Indeed, Medical Aid expenditures of the top 2% of Medical Aid users in 2005 were 280% of the expen-diture average of the total of all Medical Aid users in Korea [11]. The second have high possi-bility of over treatment by providers, as Medical Aid reimbursement is based on fee-for-service in South Korea [12]. Despite these facts, the government supports low-income groups who are not covered by standard health insurance.
Given this situation, it was no surprise to find the Korean government initiating strategies for rational management of over-users [13]. Underuse of medical services could render pa-tients’conditions worse than their current health status, which could later lead to higher long-term health care costs. This reinforces the need for education on the optimal use of Medical Aid services [12]. Medical Aid for the poor should also provide adequate medical services, but overuesr should be avoided. Beneficiaries need to learn what optimal service is and how to get it, and providers need to make appropriate decisions for the care of recipients [12].
In addition, introduction of the US case management system in 2003 was another means to curb the cost of overuse and to improve treatment outcomes and patient satisfaction [14,15]. We should also consider the integration of NHI and Medical Aid. The high spending by Medi-cal Aid must be controlled, and there are some disparities with low income NHI beneficiaries who do not qualify for Medical Aid. Integration would solve these problems by implementing the same controlling mechanism for Medical Aid as the NHI.
Health status indicators appeared to be strong determinants of the number of physician vis-its and showed the expected positive and significant impact on the medical care demand. Therefore, cost sharing policies in Korean health insurance should be necessary because the re-lation of price and utilization of medical services in the real world should be an important fac-tor in deciding utilization of medical services[16].
Although our additional analysis was performed as shown in Appendix 1, further studies are required for precisely identifying the possibility of unmet needs due to economic barrier of Health Insurance beneficiaries who do not qualify for Medical Aid and the possibility of moral hazards of Medical Aid beneficiaries with high health insurance coverage levels.
Table 5. Adjusted effect of study variables on health care utilization.
Number of outpatient visit Number of hospitalization Days spent in hospital
RR 95% CI P-value RR 95% CI P-value RR 95% CI P-value
Medical Security
Health Insurance 1.000 1.000 1.000
Medical Aid 1.431 1.225 1.672 <.0001 1.604 1.268 2.030 <.0001 1.282 0.826 1.991 0.268
Gender
Male 1.000 1.000 1.000
Female 1.202 1.053 1.373 0.007 0.836 0.690 1.013 0.068 0.407 0.267 0.621 <.0001
Age
19 1.000 1.000 1.000
20–39 0.797 0.578 1.100 0.168 1.229 0.668 2.262 0.507 3.923 1.403 10.970 0.009
40–59 1.367 1.050 1.780 0.020 2.186 1.418 3.371 0.000 4.898 2.038 11.775 0.000
60 1.584 1.291 1.944 <.0001 2.076 1.348 3.198 0.001 4.451 1.641 12.072 0.003
Residential region
Urban 1.103 0.959 1.267 0.170 0.789 0.644 0.967 0.022 0.734 0.517 1.042 0.084
Rural 1.000 1.000 1.000
Marital status
Married 0.951 0.810 1.117 0.541 0.910 0.731 1.131 0.395 1.021 0.666 1.567 0.923
Single 1.000 1.000 1.000
Education level
Elementary 1.405 0.968 2.041 0.074 0.807 0.479 1.359 0.420 2.407 1.113 5.206 0.026
Middle school 1.066 0.727 1.563 0.742 0.764 0.438 1.332 0.343 2.229 0.980 5.071 0.056
High school 0.952 0.660 1.372 0.791 0.579 0.337 0.993 0.047 1.302 0.590 2.870 0.513
College 1.000 1.000 1.000
Ordinary income
Low 1.163 0.930 1.454 0.185 1.614 1.008 2.586 0.046 1.880 0.942 3.752 0.073
Middle 1.161 0.953 1.414 0.139 1.886 1.178 3.017 0.008 2.974 1.602 5.523 0.001
High 1.000 1.000 1.000
Number of chronic disease
No 1.000 1.000 1.000
Yes 2.246 1.833 2.752 <.0001 6.683 4.591 9.728 <.0001 12.785 6.357 25.716 <.0001
Duration of medical treatment
Nothing 1.000 1.000 1.000
3 month or less 0.933 0.735 1.184 0.567 0.638 0.404 1.008 0.054 0.421 0.176 1.012 0.053
3–6 month 1.976 1.196 3.264 0.008 0.878 0.548 1.407 0.589 0.516 0.211 1.261 0.147
6 month or more 1.789 1.464 2.186 <.0001 0.641 0.457 0.900 0.010 0.649 0.288 1.464 0.298
Economic activity status
No (including housewife) 1.149 0.962 1.372 0.125 1.810 1.427 2.297 <.0001 3.810 2.302 6.309 <.0001
White collar 1.099 0.839 1.439 0.493 1.393 0.924 2.100 0.114 1.893 1.012 3.543 0.046
Blue collar 1.000 1.000 1.000
Unmet need experience
Yes 1.357 1.052 1.752 0.019 1.173 0.804 1.711 0.409 1.112 0.437 2.831 0.824
No 1.000 1.000 1.000
Year
2008 1.000 1.000 1.000
2009 0.991 0.884 1.112 0.882 1.275 1.036 1.570 0.022 1.174 0.772 1.785 0.453
2010 1.205 1.061 1.368 0.004 1.176 0.967 1.431 0.104 1.608 1.030 2.512 0.037
2011 1.170 1.025 1.334 0.020 1.058 0.847 1.322 0.619 1.282 0.801 2.051 0.300
2012 1.100 0.976 1.239 0.119 1.229 0.992 1.524 0.060 1.371 0.817 2.300 0.233
Conclusion
There are differences in health care utilization between Medical Aid beneficiaries and Health Insurance beneficiaries. More health care utilization by Medical Aid beneficiaries appears to re-sult in a high possibility for moral hazard under health insurance programs in response to the expanded benefits of health insurance coverage. Therefore, Medical Aid for the poor should provide adequate medical services.
Key Message
• Medical Aid beneficiaries tended to have an increased number of outpatient visits, hospitali-zations, and days spent in hospitals compared to Health Insurance beneficiaries who have relatively lower medical service coverage.
• It seems to indicate that an unmet needs due to economic barrier for Medical Aid beneficia-ries on medical care utilization does not exist.
Author Contributions
Conceived and designed the experiments: JHK. Performed the experiments: KSL. Analyzed the data: JHK KBY. Contributed reagents/materials/analysis tools: ECP. Wrote the paper: JHK. Contributed to the discussion: JHK. Reviewed the overall manuscript: JHK.
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Table 6. Adjusted effect of subgroup analysis according to economic activity status.
Number of outpatient visit Number of hospitalization Days spent in hospital
RR 95% CI P-value RR 95% CI P-value RR 95% CI P-value
Medical Security (No)
Health Insurance ref ref ref
Medical Aid 1.399 1.184 1.653 <.0001 1.612 1.231 2.110 0.001 1.296 0.764 2.199 0.336
Medical Security (White collar)
Health Insurance ref ref ref
Medical Aid 1.518 0.849 2.715 0.159 1.473 0.757 2.866 0.254 0.738 0.234 2.323 0.603
Medical Security (Blue collar)
Health Insurance ref ref ref
Medical Aid 1.252 0.892 1.758 0.193 1.206 0.709 2.053 0.490 1.225 0.590 2.545 0.586
Adjusted for gender, age, residential region, marital status, education level, ordinary income, number of chronic disease, duration of medical treatment, unmet need experience and year
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